Learn How to Make AI-Driven Decisions

Foundations of Data Science and AI 

• Introduction to the World of Data 
• Introduction to Python 
• Introduction to Generative AI 
• Applications of Data Science and AI 
• Data Science Lifecycle 
• Mathematics and Statistics Behind Data Science and AI 
• History of Data Science and AI

This module provides an in-depth exploration of the entire lifecycle of an AI application through detailed case study analysis. By examining real-world scenarios, you will gain a comprehensive understanding of how AI tackles complex business challenges, equipping you with insights into AI's role in driving business solutions from conception to execution.

This course focuses on essential data science skills, laying the foundation for understanding artificial intelligence. You will learn to efficiently manage and analyze data using Python's NumPy and Pandas libraries, create data visualizations, and apply statistical techniques like inferential statistics and hypothesis testing to draw meaningful insights from data.

Explore how Generative AI can analyze and synthesize unstructured data, empowering smarter, faster decision-making in a masterclass.

This course focuses on the techniques to extract insights from unstructured data. You will learn clustering methods to group similar data points and apply dimensionality reduction techniques such as PCA and t-SNE, simplifying complex data while preserving essential patterns and relationships.

• Supervised & Unsupervised Learning 
• K-Means Clustering 
• Dimensionality Reduction techniques - PCA, t-SNE

This course explores the fundamentals of regression and prediction, guiding you through linear and non-linear regression methods to model relationships in data. You will delve into causal inference to understand cause-and-effect relationships, tackle high-dimensional data challenges with regularization techniques, and refine your skills in model evaluation through cross-validation and bootstrapping, ensuring accurate and reliable predictive models.

This course offers an overview of classification and hypothesis testing techniques. You will learn classification methods such as logistic regression, decision trees, and random forests, along with understanding Type 1 and Type 2 errors in classification. The course also includes essential hypothesis testing skills to validate models and interpret data results effectively.

• Introduction to Classification 
• Logistic Regression 
• Decision Trees and Random Forest 
• Type 1 Error & Type 2 Error 
•  Hypothesis Testing

Consolidate your learning through a hands-on classification project and explore text labeling with Generative AI techniques.

• Project on Machine Learning Classification 
• Masterclass on AI-Powered Text Labeling

This course covers the essentials of deep learning and computer vision. You will learn about neural networks, from basic single-layer models to complex multi-layer architectures for multi-class predictions. Explore computer vision, focusing on CNNs, their architecture, and key concepts, while also understanding transfer learning for efficient image processing.

• Introduction to Deep Learning 
• Neural Network Representations 
• Introduction to Computer Vision 
• CNN architecture 
• Transfer Learning

This course delves into the core concepts of recommendation systems, exploring techniques like clustering, collaborative filtering, and singular value thresholding to create personalized user experiences effectively.

• Recommendation Systems 
• Types of Recommendation Systems 
• Clustering 
• Collaborative Filtering 
• Single Value Thresholding

In this course, you will explore the AI lifecycle, identify bias with real-world examples, and understand causality and privacy concerns. The course also covers the interconnections and domains of AI systems, focusing on their interdependency and feedback mechanisms to ensure responsible AI development and deployment.

• Introduction to AI Lifecycle 
• Introduction to Bias and its Examples 
• Introduction to Causality and Privacy 
• Interconnections and Domains 
• Interdependency and Feedback in AI Systems

Combine all your skills in a final project, building an end-to-end data-driven solution.

• Project on Recommendation System
  

Extend your skills in specialized areas through optional, advanced modules.